Characterizing age fromhandwriting (HW) has important applications, as it is key to distinguishing normalHWevolution with age fromabnormalHWchange, potentially triggered by neurodegenerative decline.We propose, in this work, an original approach for onlineHWstyle characterization based on a two-level clustering scheme.The first level generates writer-independent word clusters from raw spatial-dynamic HW information. At the second level, each writer's words are converted into a Bag of Prototype Words that is augmented by an interword stability measure.This two-level HWstyle representation is input to an unsupervised learning technique, aiming at uncovering HWstyle categories and their correlation with age. To assess the effectiveness of our approach, we propose information theoretic measures to quantify the gain on age information from each clustering layer. We have carried out extensive experiments on a large public online HWdatabase, augmented byHWsamples acquired at Broca Hospital in Paris from people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age, our study reveals three major aging HW styles, one specific to aged people and the two others shared by other age groups